Internet of Things (IoT) is gaining more attention from last few decades. Nowadays, people are moving towards the IoT based systems for living their life luxuriously. Adoption of IoT in the field of healthcare sector is noticeable. Real time monitoring of patient is possible in better way using this technique. Due to integration of IoT, the quality of services in healthcare field is surpasses. Most of the time due to improper monitoring of patient's body parameters causes hazardous effects on patient's health. In this paper, we discuss the IoT based remote monitoring of patient. The aim here is to get proper and timely treatment to the patient, when any of the health parameters crosses its set limits. The abnormal condition of patient is informed to his physician, care taker and family members by sending message about abnormal health parameter. So that patient can be treated well in time.

Digital security plays an ever-increasing, crucial role in today’s information-based society. The variety of threats and attack patterns has dramatically increased with the advent of digital transformation in our lives. Researchers in both public and private sectors have tried to identify new means to counteract these threats, seeking out-of-the-box ideas and novel approaches. Amongst these, data analytics and artificial intelligence/machine learning tools seem to gain new ground in digital defence. However, such instruments are used mainly offline with the purpose of auditing existing IDS/IDPS solutions. We submit a novel concept for integrating machine learning and analytical tools into a live intrusion detection and prevention solution. This approach is named the Experimental Cyber Attack Detection Framework (ECAD). The purpose of this framework is to facilitate research of on-the-fly security applications. By integrating offline results in real-time traffic analysis, we could determine the type of network access as a legitimate or attack pattern, and discard/drop the latter. The results are promising and show the benefits of such a tool in the early prevention stages of both known and unknown cyber-attack patterns.

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Designing a security solution should rely on having a good knowledge of the protected assets and better develop active responses rather than focus on reactive ones. We argue and prove that malicious activities such as vulnerabilities exploitation and (D)DoS on Web applications can be detected during their respective initial phases. While they may seem distinct, both attack scenarios are observable through abnormal access patterns. Following on this remark, we first analyze Web access logs using association rule mining techniques and identify these malicious traces. This new description of the historical data is then correlated with Web site structure information and mapped over trie data structures. The resulted trie is then used for every new incoming request and we thus identify whether the access pattern is legitimate or not. The results we obtained using this proactive approach show that the potential attacker is denied the required information for orchestrating successful assaults.